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R Programming for Data Science

Overview The R Programming for Data Science course provides a comprehensive introduction to the R programming language, focusing on its …

R Programming for Data Science

R Programming for Data Science

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R Programming for Data Science Overview

The R Programming for Data Science course provides a comprehensive introduction to the R programming language, focusing on its applications in data science. This course covers fundamental programming concepts, data manipulation techniques, and advanced topics essential for data analysis. Participants will learn how to use R and RStudio, manipulate data structures, perform data cleaning, and create visualisations. By the end of this course, students will be equipped with the skills necessary to tackle real-world data science challenges using R.

 

Learning Outcomes

  • Understand the basic concepts of data science and the role of R in data analysis.
  • Navigate and utilise R and RStudio for data science tasks.
  • Master basic R syntax and data types, including vectors, matrices, factors, and data frames.
  • Apply relational and logical operators for data manipulation.
  • Implement conditional statements and loops to control the flow of programmes.
  • Create and use functions to streamline repetitive tasks.
  • Explore and utilise R packages to extend R’s functionality.
  • Employ the Apply family of functions (lapply, sapply, vapply) for efficient data processing.
  • Use regular expressions for pattern matching and text manipulation.
  • Perform data cleaning, manipulation with dplyr, and data visualisation in R.

Who Is This Course For

This course is designed for individuals who are interested in pursuing a career in data science or enhancing their data analysis skills. It is ideal for beginners with no prior experience in R programming, as well as professionals seeking to expand their expertise in data manipulation and visualisation using R. Whether you are a student, an aspiring data scientist, or a professional looking to leverage data science in your field, this course will provide you with the necessary tools and knowledge to succeed.

 

Eligibility Requirements

Participants should understand basic programming concepts and be familiar with data analysis. While you don’t need prior experience in R, having a foundational knowledge of mathematics and statistics will be beneficial. Bring your passion for learning and interest in data science to make the most out of this course.

Entry Requirements

  • Age Requirement: Applicants must be aged 16 or above, allowing both young learners and adults to engage in this educational pursuit.
  • Academic Background: There are no specific educational prerequisites, opening the door to individuals from diverse academic histories.
  • Language Proficiency: A good command of the English language is essential for comprehension and engagement with the course materials.
  • Numeracy Skills: Basic numeracy skills are required for understanding nutritional data and dietary planning.

Why Choose Us

  • Affordable, engaging & high-quality e-learning study materials;
  • Tutorial videos/materials from the industry-leading experts;
  • Study in a user-friendly, advanced online learning platform;
  • Efficient exam systems for the assessment and instant result;
  • The UK & internationally recognised accredited
  • Access to course content on mobile, tablet or desktop from anywhere, anytime;
  • The benefit of career advancement opportunities;
  • 24/7 student support via email.

Career Path

Completing this course opens up various career opportunities in the field of data science and analytics. With the skills acquired, you can pursue roles such as data analyst, data scientist, business analyst, and more. Additionally, the knowledge gained from this course will enable you to contribute to data-driven decision-making processes within organisations, making you a valuable asset in today’s data-centric world. The foundational skills in R programming and data manipulation will also pave the way for advanced studies and specialisations in data science.

 

Course Curriculum

Unit 01: Data Science Overview
Introduction to Data Science 00:01:00
Data Science: Career of the Future 00:04:00
What is Data Science? 00:02:00
Data Science as a Process 00:02:00
Data Science Toolbox 00:03:00
Data Science Process Explained 00:05:00
What’s Next? 00:01:00
Unit 02: R and RStudio
Engine and coding environment 00:03:00
Installing R and RStudio 00:04:00
RStudio: A quick tour 00:04:00
Unit 03: Introduction to Basics
Arithmetic with R 00:03:00
Variable assignment 00:04:00
Basic data types in R 00:03:00
Unit 04: Vectors
Creating a vector 00:05:00
Naming a vector 00:04:00
Vector selection 00:06:00
Selection by comparison 00:04:00
Unit 05: Matrices
What’s a Matrix? 00:02:00
Analyzing Matrices 00:03:00
Naming a Matrix 00:05:00
Adding columns and rows to a matrix 00:06:00
Selection of matrix elements 00:03:00
Arithmetic with matrices 00:07:00
Additional Materials 00:00:00
Unit 06: Factors
What’s a Factor? 00:02:00
Categorical Variables and Factor Levels 00:04:00
Summarizing a Factor 00:01:00
Ordered Factors 00:05:00
Unit 07: Data Frames
What’s a Data Frame? 00:03:00
Creating Data Frames 00:20:00
Selection of Data Frame elements 00:03:00
Conditional selection 00:03:00
Sorting a Data Frame 00:03:00
Additional Materials 00:00:00
Unit 08: Lists
Why would you need lists? 00:01:00
Creating a List 00:06:00
Selecting elements from a list 00:03:00
Adding more data to the list 00:02:00
Additional Materials 00:00:00
Unit 09: Relational Operators
Equality 00:03:00
Greater and Less Than 00:03:00
Compare Vectors 00:03:00
Compare Matrices 00:02:00
Additional Materials 00:00:00
Unit 10: Logical Operators
AND, OR, NOT Operators 00:04:00
Logical operators with vectors and matrices 00:04:00
Reverse the result: (!) 00:01:00
Relational and Logical Operators together 00:06:00
Additional Materials 00:00:00
Unit 11: Conditional Statements
The IF statement 00:04:00
IF…ELSE 00:03:00
The ELSEIF statement 00:05:00
Full Exercise 00:03:00
Additional Materials 00:00:00
Unit 12: Loops
Write a While loop 00:04:00
Looping with more conditions 00:04:00
Break: stop the While Loop 00:04:00
What’s a For loop? 00:02:00
Loop over a vector 00:02:00
Loop over a list 00:03:00
Loop over a matrix 00:04:00
For loop with conditionals 00:01:00
Using Next and Break with For loop 00:03:00
Additional Materials 00:00:00
Unit 13: Functions
What is a Function? 00:02:00
Arguments matching 00:03:00
Required and Optional Arguments 00:03:00
Nested functions 00:02:00
Writing own functions 00:03:00
Functions with no arguments 00:02:00
Defining default arguments in functions 00:04:00
Function scoping 00:02:00
Control flow in functions 00:03:00
Additional Materials 00:00:00
Unit 14: R Packages
Installing R Packages 00:01:00
Loading R Packages 00:04:00
Different ways to load a package 00:02:00
Additional Materials 00:00:00
Unit 15: The Apply Family - lapply
What is lapply and when is used? 00:04:00
Use lapply with user-defined functions 00:03:00
lapply and anonymous functions 00:01:00
Use lapply with additional arguments 00:04:00
Additional Materials 00:00:00
Unit 16: The apply Family – sapply & vapply
What is sapply? 00:02:00
How to use sapply 00:02:00
sapply with your own function 00:02:00
sapply with a function returning a vector 00:02:00
When can’t sapply simplify? 00:02:00
What is vapply and why is it used? 00:04:00
Additional Materials 00:00:00
Unit 17: Useful Functions
Mathematical functions 00:05:00
Data Utilities 00:08:00
Additional Materials 00:00:00
Unit 18: Regular Expressions
grepl & grep 00:04:00
Metacharacters 00:05:00
sub & gsub 00:02:00
More metacharacters 00:04:00
Additional Materials 00:00:00
Unit 19: Dates and Times
Today and Now 00:02:00
Create and format dates 00:06:00
Create and format times 00:03:00
Calculations with Dates 00:03:00
Calculations with Times 00:07:00
Additional Materials 00:00:00
Unit 20: Getting and Cleaning Data
Get and set current directory 00:04:00
Get data from the web 00:04:00
Loading flat files 00:03:00
Loading Excel files 00:05:00
Additional Materials 00:00:00
Unit 21: Plotting Data in R
Base plotting system 00:03:00
Base plots: Histograms 00:03:00
Base plots: Scatterplots 00:05:00
Base plots: Regression Line 00:03:00
Base plots: Boxplot 00:03:00
Unit 22: Data Manipulation with dplyr
Introduction to dplyr package 00:04:00
Using the pipe operator (%>%) 00:02:00
Columns component: select() 00:05:00
Columns component: rename() and rename_with() 00:02:00
Columns component: mutate() 00:02:00
Columns component: relocate() 00:02:00
Rows component: filter() 00:01:00
Rows component: slice() 00:04:00
Rows component: arrange() 00:01:00
Rows component: rowwise() 00:02:00
Grouping of rows: summarise() 00:03:00
Grouping of rows: across() 00:02:00
COVID-19 Analysis Task 00:08:00
Additional Materials 00:00:00
Assignment
Assignment – R Programming for Data Science 00:00:00

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